Affiliation:
1. Graduate School of Business, Stanford University, Stanford, CA, USA.
Abstract
Machine-learning prediction methods have been extremely productive in applications ranging from medicine to allocating fire and health inspectors in cities. However, there are a number of gaps between making a prediction and making a decision, and underlying assumptions need to be understood in order to optimize data-driven decision-making.
Publisher
American Association for the Advancement of Science (AAAS)
Cited by
342 articles.
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