One of the challenges practitioners face when applying structure learning
algorithms to their data involves determining a set of hyperparameters;
otherwise, a set of hyperparameter defaults is assumed. The optimal
hyperparameter configuration often depends on multiple factors, including the
size and density of the usually unknown underlying true graph, the sample size
of the input data, and the structure learning algorithm. We propose a novel
hyperparameter tuning method, called the Out-of-sample Tuning for Structure
Learning (OTSL), that employs out-of-sample and resampling strategies to
estimate the optimal hyperparameter configuration for structure learning, given
the input data set and structure learning algorithm. Synthetic experiments show
that employing OTSL as a means to tune the hyperparameters of hybrid and
score-based structure learning algorithms leads to improvements in graphical
accuracy compared to the state-of-the-art. We also illustrate the applicability
of this approach to real datasets from different disciplines.

本文提出一种名为 Out-of-sample Tuning for Structure Learning (OTSL) 的新型超参数调整方法，该方法使用样本外估计和重抽样策略来估计给定输入数据集和结构学习算法的最优超参数配置，并通过实验和实际数据集表明，使用 OTSL 调整混合和基于分数的结构学习算法的超参数可以提高图形准确性。