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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.doi:10.1126/science.aal4321 pmid:28154050 fatcat:pdzzzzx2c5bvxijpy5jfhkkt2y