Predictive machine learning for prescriptive applications: a coupled training-validating approach

TL;DR

This paper introduces a coupled validation approach for training predictive machine learning models that optimizes hyper-parameters based on prescription loss, improving prescriptive decision-making in various applications.

Contribution

It presents a novel coupled validation method that incorporates prescription loss into hyper-parameter tuning, enhancing prescriptive accuracy without complex model-specific adjustments.

Findings

Reduces prescription costs in synthetic and real data experiments

Applicable to various machine learning models, including hybrid prediction-stochastic-optimization

Demonstrates improved decision quality in prescriptive applications

Abstract

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.