论文标题
通过多项式可伸缩性的可解释性
Scalable Interpretability via Polynomials
论文作者
论文摘要
通用的加性模型（GAM）已迅速成为固有破解的机器学习的主要选择。但是，与不可解释的方法（例如DNNS）不同，它们缺乏表现力和易于可扩展性，因此对于实际任务而言并不是可行的替代方法。我们提出了一种新的游戏类，该类别使用张量的多项式量级分解来学习功能强大的{\ em固有解动}模型。我们的方法标题为“可扩展多项式添加剂模型（垃圾邮件”）是毫不费力的可扩展性，并且模型{\ em all}的高阶特征交互无组合参数爆炸。垃圾邮件的表现优于所有当前可解释的方法，并在一系列现实世界中的基准测试中匹配DNN/XGBOOST性能，最多可提供数十万个功能。我们通过人类主题评估证明，垃圾邮件在实践中明显更容易解释，因此是DNN毫不费力的替代者，用于创建适合大规模机器学习的可解释和高性能系统。源代码可在https://github.com/facebookresearch/nbm-pam上获得。
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.
