Validation of scenario generation for decision-making using machine learning prediction models

Machine learning provides valuable information for data-driven decision-making. However, real-world problems commonly include uncertainties and the features needed to generate the prediction outputs are random variables. Even the most reliable machine learning models may not be helpful for decision-makers when the decisions must be taken before the values of features used in machine learning models are realized. To support decision-making under uncertainty, we propose a scenario generation procedure for stochastic programs that incorporates the uncertainties in both prediction features and the machine learning model prediction error. A statistical test is implemented to assess the reliability of the scenario sets by comparison with corresponding historical observations. We test the whole procedure in a case study for crop yield in Midwest.