Multi-Objective Optimization and Hyperparameter Tuning With Desirability Functions
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.
Code (1)
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Tuning for Trustworthiness -- Balancing Performance and Explanation Consistency in Neural Network Optimization
Despite the growing interest in Explainable Artificial Intelligence (XAI), explainability is rarely considered during hyperparameter tuning or neural architecture optimization, where the focus remains primarily on minimi…
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)Model SelectionMulti-Objective Hyperparameter Tuning and Feature Selection using Filter Ensembles
Both feature selection and hyperparameter tuning are key tasks in machine learning. Hyperparameter tuning is often useful to increase model performance, while feature selection is undertaken to attain sparse models. Spar…
feature selectionHyperparameter OptimizationComparing Scalar Objective Functions for Multi-Criteria Engineering Optimization
Scalar objective functions are required when a multi-criteria optimization problem must yield a single preferred design rather than only a Pareto set. The choice of scalarization influences which compromise is selected, …
Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning
Bayesian optimization is popular for optimizing time-consuming black-box objectives. Nonetheless, for hyperparameter tuning in deep neural networks, the time required to evaluate the validation error for even a few hyper…
Bayesian OptimizationHyperparameter Importance Analysis for Multi-Objective AutoML
Hyperparameter optimization plays a pivotal role in enhancing the predictive performance and generalization capabilities of ML models. However, in many applications, we do not only care about predictive performance but a…
AutoMLHyperparameter Optimization