Multi-Objective Optimization and Hyperparameter Tuning With Desirability Functions
This work provides a tool for practitioners in machine learning and optimization to handle multi-objective tasks, but it is incremental as it ports an existing method to a new language.
The paper introduces a Python implementation of desirability functions for multi-objective optimization and hyperparameter tuning, based on an existing R package, and demonstrates its application through three examples.
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.