For decades, prescriptive analytics—and, specifically, mathematical optimization—has been a secret weapon for businesses to address complex problems. Meanwhile, machine learning has emerged as a powerful tool for pattern recognition and prediction. But what happens when these two technologies join forces? The synergy between prescriptive analytics and machine learning opens up new possibilities for innovation and efficiency.

Three Ways to Integrate Machine Learning and Mathematical Optimization

Machine learning and mathematical optimization have often existed in silos, each with its unique strengths and applications. However, the integration of these two technologies is breaking down barriers and creating a dynamic duo that can complement and enhance each other. Here’s how:

    • Machine Learning as Input for Mathematical Optimization: Machine learning predictions can serve as valuable input for mathematical optimization solutions, providing data-driven insights to guide decision-making.
    • Mathematical Optimization as Input for Machine Learning: Conversely, mathematical optimization solutions can inform machine learning predictions, offering a structured approach to problem-solving.
    • Mathematical Optimization for Machine Learning Problems: Mathematical optimization can generate optimal solutions to classification, prediction, and other problems typically solved using machine learning, leveraging its problem-solving power to enhance machine-learning applications.

Three Key Benefits of Integrating Prescriptive Analytics with Machine Learning

Adding Semantics to Your Model

Machine learning solutions often don’t consider the semantics of the problem, leading to a learning curve for things we already know. Prescriptive analytics allows you to add semantics in the form of constraints, providing structure and ensuring that your expertise is embedded in the model.

Improving Prediction Interpretability

While machine learning often operates as a black box, the output of mathematical optimization applications is interpretable. By analyzing the model’s variables and constraints, you can pinpoint the factors driving specific solutions, improving the interpretability of predictions.

Bounding the Error of Predictions

Machine-learning-based predictions inherently contain uncertainty. By feeding the original problem into a mathematical optimization solver, you can determine how close you are to an optimal solution, bounding and minimizing the error of predictions.


As Confucius said, “You shouldn’t use a cannon to kill a fly.” Machine learning is indeed a powerful tool, but it’s not necessarily the best solution for every problem. By integrating mathematical optimization and machine learning, data scientists and businesses can discover new ways to address complex problems and enhance their applications.

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