The Voice of Optimization

Understand

We introduce the idea that using optimal classification trees (OCTs) and optimal classification trees with-hyperplanes (OCT-Hs), interpretable machine learning algorithms developed by Bertsimas and Dunn [2017, 2018], we are able to obtain insight on the strategy behind the optimal solution in continuous and mixed-integer convex optimization problem as a function of key parameters that affect the problem.

• In this way, optimization is not a black box anymore.
• Instead, we redefine optimization as a multiclass classification problem where the predictor gives insights on the logic behind the optimal solution.
• In other words, OCTs and OCT-Hs give optimization a voice.