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.

在选择适合特定的机器学习模型类和性能度量组合的合成训练任务的最佳生成模型的过程中，我们的研究发现，当模型解释性要求苛刻时，基于贝叶斯网络的生成模型优于基于神经网络的生成模型。我们的结果为有意将训练数据从真实数据集替换为合成数据集的机器学习从业者提供了实际指导，并为更一般的下游任务导向的生成模型选择问题提供了启示。