Feedforward neural networks (FNNs) are typically viewed as pure prediction algorithms, and their strong predictive performance has led to their use in many machine-learning applications. However, their flexibility comes with an interpretability trade-off; thus, FNNs have been historically less popular among statisticians. Nevertheless, classical statistical theory, such as significance testing and uncertainty quantification, is still relevant. Supplementing FNNs with methods of statistical inference, and covariate-effect visualisations, can shift the focus away from black-box prediction and make FNNs more akin to traditional statistical models. This can allow for more inferential analysis, and, hence, make FNNs more accessible within the statistical-modelling context.

翻译：前馈神经网络通常被视为纯预测算法，其强大的预测性能使其广泛应用于众多机器学习领域。然而，这种灵活性以牺牲可解释性为代价，因此前馈神经网络在统计学家中的历史接受度较低。尽管如此，经典统计理论（如显著性检验与不确定性量化）仍具有相关性。将统计推断方法与协变量效应可视化技术引入前馈神经网络，可将关注点从黑箱预测转移，使其更接近传统统计模型。这种转化有助于开展推断性分析，从而提升前馈神经网络在统计建模语境中的适用性。