Tabular Data: Is Deep Learning all you need?

TL;DR

This paper benchmarks recent deep learning models against classical ML methods on tabular data, revealing that deep learning can outperform traditional approaches in diverse real-world datasets.

Contribution

It provides a comprehensive empirical comparison of 17 methods, including neural networks and classical ML, on 68 datasets, highlighting a potential paradigm shift.

Findings

Deep learning methods outperform classical approaches on tabular data.

Neural networks show competitive performance across diverse datasets.

The study offers a benchmark for future research in tabular data modeling.

Abstract

Tabular data represent one of the most prevalent data formats in applied machine learning, largely because they accommodate a broad spectrum of real-world problems. Existing literature has studied many of the shortcomings of neural architectures on tabular data and has repeatedly confirmed the scalability and robustness of gradient-boosted decision trees across varied datasets. However, recent deep learning models have not been subjected to a comprehensive evaluation under conditions that allow for a fair comparison with existing classical approaches. This situation motivates an investigation into whether recent deep-learning paradigms outperform classical ML methods on tabular data. Our survey fills this gap by benchmarking seventeen state-of-the-art methods, spanning neural networks, classical ML and AutoML techniques. Our empirical results over 68 diverse datasets from a well-established benchmark indicate a paradigm shift, where Deep Learning methods outperform classical approaches.