Graduate School of Business, Stanford University, Stanford, CA, USA.
Science. 2017 Feb 3;355(6324):483-485. doi: 10.1126/science.aal4321. Epub 2017 Feb 2.
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.
机器学习预测方法在从医学到城市消防和卫生检查员分配等各个领域都取得了极大的成果。然而，在做出预测和做出决策之间存在着许多差距，需要理解潜在的假设，以便优化数据驱动的决策。
