Generalized Additive Models (GAMs) have quickly become the leading choice for
inherently-interpretable machine learning. However, unlike uninterpretable
methods such as DNNs, they lack expressive power and easy scalability, and are
hence not a feasible alternative for real-world tasks. We present a new class
of GAMs that use tensor rank decompositions of polynomials to learn powerful,
{\em inherently-interpretable} models. Our approach, titled Scalable Polynomial
Additive Models (SPAM) is effortlessly scalable and models {\em all}
higher-order feature interactions without a combinatorial parameter explosion.
SPAM outperforms all current interpretable approaches, and matches DNN/XGBoost
performance on a series of real-world benchmarks with up to hundreds of
thousands of features. We demonstrate by human subject evaluations that SPAMs
are demonstrably more interpretable in practice, and are hence an effortless
replacement for DNNs for creating interpretable and high-performance systems
suitable for large-scale machine learning. Source code is available at
this https URL

提出了一种新的分类 GAMs 的方法，称为 Scalable Polynomial Additive Models (SPAM)，通过多项式的张量秩分解来实现高阶特征交互，同时保证模型的可解释性和易扩展性，大幅度地优于现有的可解释方案，并在真实世界的各种基准测试中表现出与 DNN / XGBoost 相当的性能。