Multi-Objective Optimization and Hyperparameter Tuning With Desirability Functions

• URL: http://arxiv.org/abs/2503.23595v1
• Date: Sun, 30 Mar 2025 21:16:41 GMT
• Title: Multi-Objective Optimization and Hyperparameter Tuning With Desirability Functions
• Authors: Thomas Bartz-Beielstein,
• Abstract summary: The Python package spotdesirability is available as part of the sequential parameter optimization framework.<n>Three examples are given that demonstrate how to use the desirability functions for classical optimization.
• Score: 0.5439020425819
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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