Machine Learning Times
Machine Learning Times
EXCLUSIVE HIGHLIGHTS
Explainable Machine Learning, Model Transparency, and the Right to Explanation
 Check out this topical video from Predictive Analytics World...
Guidebook to the Future of Data Science: How to Navigate the Increasingly Symbiotic Dynamic Between Executives and Universities
 Book Review of Closing the Analytics Talent Gap: An...
Guilty or Not Guilty: Weight of Evidence
 You have been invited to serve as a juror...
How Machine Learning Works for Social Good
  Originally published in KDnuggets, Nov 2020. This article...
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3 months ago
Measuring Invisible Treatment Effects with Uplift Analysis

  Models make predictions by identifying consistent correlations in what has been observed, but we usually require more than predictions to know what action we should take. For example, knowing that older people are more likely to have heart disease is a good first step, but knowing behaviors or treatments that will reduce the risk of heart disease as we age is actionable. Knowing millennials are more likely to buy your product than gen Z is nice, but knowing which marketing approach will persuade gen Z to buy is valuable. In this election season, knowing who will vote

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