Scalable Interpretability via Polynomials

Our approach, titled Scalable Polynomial Additive Models (SP AM) is effortlessly scalable and models all higher-order feature interactions without a combinatorial parameter explosion. SP AM 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.