Are key-foreign key joins safe to avoid when learning high-capacity classifiers?

Abstract

Machine learning (ML) over relational data is a booming area of data management. While there is a lot of work on scalable and fast ML systems, little work has addressed the pains of sourcing data for ML tasks. Real-world relational databases typically have many tables (often, dozens) and data scientists often struggle to even obtain all tables for joins before ML. In this context, Kumar et al. showed recently that key-foreign key dependencies (KFKDs) between tables often lets us avoid such joins without significantly affecting prediction accuracy-an idea they called "avoiding joins safely." While initially controversial, this idea has since been used by multiple companies to reduce the burden of data sourcing for ML. But their work applied only to linear classifiers. In this work, we verify if their results hold for three popular high-capacity classifiers: decision trees, non-linear SVMs, and ANNs. We conduct an extensive experimental study using both real-world datasets and simulations to analyze the effects of avoiding KFK joins on such models. Our results show that these high-capacity classifiers are surprisingly and counter-intuitively more robust to avoiding KFK joins compared to linear classifiers, refuting an intuition from the prior work's analysis. We explain this behavior intuitively and identify open questions at the intersection of data management and ML theoretical research. All of our code and datasets are available for download from http://cseweb.ucsd.edu/~arunkk/hamlet.