Recent scientific advances require complex experiment design, necessitating the meticulous tuning of many experiment parameters. Tree-structured Parzen estimator (TPE) is a widely used Bayesian optimization method in recent parameter tuning frameworks such as Hyperopt and Optuna. Despite its popularity, the roles of each control parameter in TPE and the algorithm intuition have not been discussed so far. The goal of this paper is to identify the roles of each control parameter and their impacts on parameter tuning based on the ablation studies using diverse benchmark datasets. The recommended setting concluded from the ablation studies is demonstrated to improve the performance of TPE. Our TPE implementation used in this paper is available at https://github.com/nabenabe0928/tpe/tree/single-opt.

翻译：近年来科学研究的进展需要复杂的实验设计，这要求对众多实验参数进行精细调优。树结构Parzen估计器（TPE）是当前参数调优框架（如Hyperopt和Optuna）中广泛使用的贝叶斯优化方法。尽管TPE应用广泛，但其各控制参数的作用与算法原理至今尚未得到充分探讨。本文旨在基于多样化基准数据集的消融实验，明确各控制参数的作用及其对参数调优的影响。通过消融研究得出的推荐配置被证实能够提升TPE的性能。本文使用的TPE实现代码已发布于https://github.com/nabenabe0928/tpe/tree/single-opt。