Generalized additive models (GAMs) have long been a powerful white-box tool for the intelligible analysis of tabular data, revealing the influence of each feature on the model predictions. Despite the success of neural networks (NNs) in various domains, their application as NN-based GAMs in tabular data analysis remains suboptimal compared to tree-based ones, and the opacity of encoders in NN-GAMs also prevents users from understanding how networks learn the functions. In this work, we propose a new deep tabular learning method, termed Prototypical Neural Additive Model (ProtoNAM), which introduces prototypes into neural networks in the framework of GAMs. With the introduced prototype-based feature activation, ProtoNAM can flexibly model the irregular mapping from tabular features to the outputs while maintaining the explainability of the final prediction. We also propose a gradient-boosting inspired hierarchical shape function modeling method, facilitating the discovery of complex feature patterns and bringing transparency into the learning process of each network layer. Our empirical evaluations demonstrate that ProtoNAM outperforms all existing NN-based GAMs, while providing additional insights into the shape function learned for each feature. The source code of ProtoNAM is available at \url{https://github.com/Teddy-XiongGZ/ProtoNAM}.

翻译：广义可加模型（GAMs）长期以来一直是分析表格数据的强大白盒工具，能够揭示每个特征对模型预测的影响。尽管神经网络（NNs）在各个领域取得了成功，但与基于树的方法相比，神经网络作为基于NN的GAMs在表格数据分析中的应用仍不理想，且NN-GAMs中编码器的不透明性也阻碍了用户理解网络如何学习函数。在本工作中，我们提出了一种新的深度表格学习方法，称为原型神经可加模型（ProtoNAM），该方法在GAMs框架中向神经网络引入了原型。通过引入基于原型的特征激活，ProtoNAM能够灵活建模从表格特征到输出的不规则映射，同时保持最终预测的可解释性。我们还提出了一种受梯度提升启发的分层形状函数建模方法，促进复杂特征模式的发现，并将透明度引入每个网络层的学习过程。我们的实证评估表明，ProtoNAM优于所有现有的基于NN的GAMs，同时为每个特征学习到的形状函数提供了额外的见解。ProtoNAM的源代码可在\url{https://github.com/Teddy-XiongGZ/ProtoNAM}获取。