xRFM: Accurate, scalable, and interpretable feature learning models for tabular data

Daniel Beaglehole, David Holzmüller, Adityanarayanan Radhakrishnan, Mikhail Belkin

Submission Type: Short paper (4 pages)

Keywords: Tabular data, kernel machines, feature learning

TL;DR: We introduce an algorithm xRFM that combines feature learning kernel machines and tree structures to achieve state-of-the-art performance on tabular data.

Abstract: Inference from tabular data, collections of continuous and categorical variables organized into matrices, is a foundation for modern technology and science. Yet, in contrast to the explosive changes in the rest of AI, the best practice for these predictive tasks has been relatively unchanged and is still primarily based on variations of Gradient Boosted Decision Trees (GBDTs). Very recently, there has been renewed interest in developing state-of-the-art methods for tabular data based on recent developments in neural networks and feature learning methods. In this work, we introduce xRFM, an algorithm that combines feature learning kernel machines with a tree structure to scale to essentially unlimited amounts of training data. On the TALENT benchmark, we show that xRFM achieves best performance across $100$ regression datasets and is competitive to the best methods across $200$ classification datasets outperforming GBDTs.

Submission Number: 4