Due to the widespread use of complex machine learning models in real-world applications, it is becoming critical to explain model predictions. However, these models are typically black-box deep neural networks, explained post-hoc via methods with known faithfulness limitations. Generalized Additive Models (GAMs) are an inherently interpretable class of models that address this limitation by learning a non-linear shape function for each feature separately, followed by a linear model on top. However, these models are typically difficult to train, require numerous parameters, and are difficult to scale. We propose an entirely new subfamily of GAMs that utilizes basis decomposition of shape functions. A small number of basis functions are shared among all features, and are learned jointly for a given task, thus making our model scale much better to large-scale data with high-dimensional features, especially when features are sparse. We propose an architecture denoted as the Neural Basis Model (NBM) which uses a single neural network to learn these bases. On a variety of tabular and image datasets, we demonstrate that for interpretable machine learning, NBMs are the state-of-the-art in accuracy, model size, and, throughput and can easily model all higher-order feature interactions. Source code is available at https://github.com/facebookresearch/nbm-spam.

翻译：由于在现实世界应用中广泛使用复杂的机器学习模型,因此解释模型预测变得至关重要。然而,这些模型通常是黑箱深神经网络,通过已知忠诚限制的方法解释后热力网络。通用Additive模型(GAMS)是解决这一局限性的内在可解释模型类别,通过对每个特性分别学习非线性形状功能,然后在顶部采用线性模型。然而,这些模型通常难以培训,需要许多参数,并且难以规模化。我们提议了全新的GAMS子系列,利用形状功能的分解基础。所有功能之间共享少量基础功能,并为某项特定任务共同学习,从而使我们的模型规模比具有高度特征的大型数据要好得多,特别是在特征稀少时。我们建议了以神经基础模型(NBM)命名的结构,该模型使用单一的神经网络来学习这些基础。在各种表格和图像数据集中,我们展示了可用于解释的机器学习、NBMS-模型(NBMS)是州/州/州际版本的模型/州际模型。通过模型/州际的精确度,所有模型/州际的版本。