We study the use of binary activated neural networks as interpretable and explainable predictors in the context of regression tasks on tabular data; more specifically, we provide guarantees on their expressiveness, present an approach based on the efficient computation of SHAP values for quantifying the relative importance of the features, hidden neurons and even weights. As the model's simplicity is instrumental in achieving interpretability, we propose a greedy algorithm for building compact binary activated networks. This approach doesn't need to fix an architecture for the network in advance: it is built one layer at a time, one neuron at a time, leading to predictors that aren't needlessly complex for a given task.

翻译：本研究探讨了在表格数据回归任务中，使用二元激活神经网络作为可解释与可解释预测因子的方法；具体而言，我们为其表达能力提供了理论保证，并提出了一种基于高效计算SHAP值的方法，以量化特征、隐藏神经元乃至权重的相对重要性。鉴于模型简洁性对实现可解释性至关重要，我们提出了一种贪心算法来构建紧凑的二元激活网络。该方法无需预先固定网络架构：模型以逐层、逐神经元的方式构建，从而确保预测因子不会因特定任务而产生不必要的复杂性。