Devising procedures for downstream task-oriented generative model selections is an unresolved problem of practical importance. Existing studies focused on the utility of a single family of generative models. They provided limited insights on how synthetic data practitioners select the best family generative models for synthetic training tasks given a specific combination of machine learning model class and performance metric. In this paper, we approach the downstream task-oriented generative model selections problem in the case of training fraud detection models and investigate the best practice given different combinations of model interpretability and model performance constraints. Our investigation supports that, while both Neural Network(NN)-based and Bayesian Network(BN)-based generative models are both good to complete synthetic training task under loose model interpretability constrain, the BN-based generative models is better than NN-based when synthetic training fraud detection model under strict model interpretability constrain. Our results provides practical guidance for machine learning practitioner who is interested in replacing their training dataset from real to synthetic, and shed lights on more general downstream task-oriented generative model selection problems.

翻译：制定面向下游任务的生成模型选择策略是一个具有实际重要性的未解问题。现有研究主要关注单一类型生成模型的效用，对于合成数据从业者如何根据特定的机器学习模型类别与性能指标组合，选择最优的生成模型家族用于合成训练任务，提供的见解有限。本文针对训练欺诈检测模型的场景，探讨面向下游任务的生成模型选择问题，并研究在不同模型可解释性与性能约束组合下的最佳实践。我们的研究支持以下结论：在模型可解释性约束较宽松的条件下，基于神经网络（NN）和基于贝叶斯网络（BN）的生成模型均能有效完成合成训练任务；但在严格模型可解释性约束下，基于BN的生成模型优于基于NN的生成模型。本研究结果为希望将训练数据集从真实数据替换为合成数据的机器学习从业者提供了实践指导，并为更广泛的面向下游任务的生成模型选择问题提供了启示。