Predictive machine learning for prescriptive applications: a coupled training-validating approach
This addresses the challenge of improving decision-making in prescriptive applications for users of machine learning, though it appears incremental as it tweaks an existing validation step.
The paper tackles the problem of training predictive models for prescriptive applications by proposing a coupled validation method that uses prescription loss for hyper-parameter calibration, resulting in reduced prescription costs in experiments with synthetic and real data.
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.