Deep neural networks (DNNs) have proven to be highly effective in a variety of tasks, making them the go-to method for problems requiring high-level predictive power. Despite this success, the inner workings of DNNs are often not transparent, making them difficult to interpret or understand. This lack of interpretability has led to increased research on inherently interpretable neural networks in recent years. Models such as Neural Additive Models (NAMs) achieve visual interpretability through the combination of classical statistical methods with DNNs. However, these approaches only concentrate on mean response predictions, leaving out other properties of the response distribution of the underlying data. We propose Neural Additive Models for Location Scale and Shape (NAMLSS), a modelling framework that combines the predictive power of classical deep learning models with the inherent advantages of distributional regression while maintaining the interpretability of additive models. The code is available at the following link: https://github.com/AnFreTh/NAMpy

翻译：深度神经网络（DNNs）已在多种任务中展现出卓越效能，成为需要高预测能力问题的首选方法。尽管取得如此成功，DNNs的内部运作机制往往缺乏透明度，导致其难以被解释或理解。这种可解释性的缺失促使近年来对固有可解释神经网络的研究日益增多。诸如神经加性模型（NAMs）等模型通过将经典统计方法与DNNs相结合，实现了视觉可解释性。然而，这些方法仅关注均值响应预测，忽略了底层数据响应分布的其他属性。我们提出位置尺度与形状的神经加性模型（NAMLSS），这是一种建模框架，既融合了经典深度学习模型的预测能力与分布回归的固有优势，又保持了加性模型的可解释性。相关代码可通过以下链接获取：https://github.com/AnFreTh/NAMpy