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：我们研究将二元激活神经网络作为可解释且可说明的预测器，应用于表格数据的回归任务；具体而言，我们为其表达性提供了理论保证，并提出了一种基于高效计算SHAP值的方法，用于量化特征、隐藏神经元甚至权重的相对重要性。由于模型的简洁性有助于实现可解释性，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：它逐层、逐神经元地构建网络，从而为给定任务生成不具多余复杂性的预测器。