  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
https://github.com/facebookresearch/nbm-spam.
