Tabular Data: Is Deep Learning all you need?

• URL: http://arxiv.org/abs/2402.03970v3
• Date: Sun, 05 Oct 2025 14:39:10 GMT
• Title: Tabular Data: Is Deep Learning all you need?
• Authors: Guri Zabërgja, Arlind Kadra, Christian M. M. Frey, Josif Grabocka,
• Abstract summary: Tabular data represents one of the most prevalent data formats in applied machine learning.<n>Deep learning models have not been subjected to a comprehensive evaluation under conditions that allow for a fair comparison with existing classical approaches.<n>Our survey fills this gap by benchmarking seventeen state-of-the-art methods, spanning neural networks, classical ML and AutoML techniques.
• Score: 21.994741050350132
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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