| Literature DB >> 28154050 |
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.Year: 2017 PMID: 28154050 DOI: 10.1126/science.aal4321
Source DB: PubMed Journal: Science ISSN: 0036-8075 Impact factor: 47.728