We investigate the impact of dataset-specific hyperparameter, feature encoding, and architecture tuning on five recent model families for tabular data generation through an extensive benchmark on 16 datasets. This study addresses the practical need for a unified evaluation of models that fully considers hyperparameter optimization. Additionally, we propose a reduced search space for each model that allows for quick optimization, achieving nearly equivalent performance at a significantly lower cost.Our benchmark demonstrates that, for most models, large-scale dataset-specific tuning substantially improves performance compared to the original configurations. Furthermore, we confirm that diffusion-based models generally outperform other models on tabular data. However, this advantage is not significant when the entire tuning and training process is restricted to the same GPU budget for all models.

通过在16个数据集上进行全面评估，我们研究了数据集特定的超参数、特征编码和架构调整对于五个最新模型家族在表格数据生成方面的影响。我们提出了一个减少的搜索空间以便进行快速优化，实现了几乎相同性能但显著降低成本。我们的评估表明，对于大多数模型而言，大规模数据集特定调优相比原始配置极大提高了性能。此外，我们证实扩散模型在表格数据上普遍优于其他模型。然而，当所有模型的调优和训练过程都限制在相同的GPU预算时，这种优势并不明显。

表格数据生成模型的内在机制: 超参数调优的强大影响