# Tabular Data: Is Deep Learning all you need?

arXiv: https://arxiv.org/abs/2402.03970 · HTML: https://mlsanity.com/paper/2402.03970/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2024.

## Built on by (most cited first, 6 of 6)

- [Beyond IID: How General Are Tabular Foundation Models, Really?](https://mlsanity.com/paper/2606.30410/) (2026)
- [Benchmarking Optimizers for MLPs in Tabular Deep Learning](https://mlsanity.com/paper/2604.15297/) (2026)
- [STRABLE: Benchmarking Tabular Machine Learning with Strings](https://mlsanity.com/paper/2605.12292/) (2026)
- [RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy](https://mlsanity.com/paper/2605.02003/) (2026)
- [TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks](https://mlsanity.com/paper/2606.02384/) (2026)
- [Peering Partner Recommendation for ISPs using Machine Learning](https://mlsanity.com/paper/2509.09146/) (2025)

## Abstract (arXiv)

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.