In this research we propose a new method for training predictive machine learning models for prescriptive applications. This approach, which we refer to as coupled validation, is based on tweaking the validation step in the standard training-validating-testing scheme. Specifically, the coupled method considers the prescription loss as the objective for hyper-parameter calibration. This method allows for intelligent introduction of bias in the prediction stage to improve decision making at the prescriptive stage, and is generally applicable to most machine learning methods, including recently proposed hybrid prediction-stochastic-optimization techniques, and can be easily implemented without model-specific mathematical modeling. Several experiments with synthetic and real data demonstrate promising results in reducing the prescription costs in both deterministic and stochastic models.
翻译:在这一研究中,我们提出了一种新的方法,用于培训用于规范应用的预测机器学习模型,我们称之为联合验证,这种方法的基础是在标准培训验证测试计划中调整验证步骤,具体地说,结合方法将处方损失视为超参数校准的目标,这种方法可以明智地在预测阶段引入偏差,以改进规范阶段的决策,并普遍适用于大多数机器学习方法,包括最近提出的混合预测-随机-优化技术,并且可以很容易地实施,而不必进行具体模型的数学模型模型。