Multi-Objective Optimization and Hyperparameter Tuning With Desirability Functions

Abstract: The goal of this article is to provide an introduction to the desirability function approach to multi-objective optimization (direct and surrogate model-based), and multi-objective hyperparameter tuning. This work is based on the paper by Kuhn (2016). It presents a Python implementation of Kuhn's R package desirability. The Python package spotdesirability is available as part of the sequential parameter optimization framework. After a brief introduction to the desirability function approach is presented, three examples are given that demonstrate how to use the desirability functions for classical optimization, surrogate-model based optimization, and hyperparameter tuning.