A Living Benchmark for Machine Learning on Tabular Data 💫

TabArena is a living benchmarking system that makes benchmarking tabular machine learning models a reliable experience. TabArena implements best practices to ensure methods are represented at their peak potential, including cross-validated ensembles, strong hyperparameter search spaces contributed by the method authors, early stopping, model refitting, parallel bagging, memory usage estimation, and more.

TabArena currently consists of:

• 51 manually curated tabular datasets representing real-world tabular data tasks.
• 9 to 30 evaluated splits per dataset.
• 16 tabular machine learning methods, including 3 tabular foundation models.
• 25,000,000 trained models across the benchmark, with all validation and test predictions cached to enable tuning and post-hoc ensembling analysis.
• A live TabArena leaderboard showcasing the results.

🕹️ Quickstart

Benchmarking and Running TabArena Models

Please refer to our example scripts for using TabArena.

Datasets

Please refer to our dataset curation repository to learn more about or contributed data!

Evaluation & Reproducing Results

To locally reproduce individual configurations and compare with the TabArena results of those configurations, refer to examples/tabarena/run_quickstart_tabarena.py.

To locally reproduce all tables and figures in the paper using the raw results data, run examples/tabarena/run_tabarena_eval.py

More Documentation

TabArena code is currently being polished. Documentation for TabArena will be available soon.

🪄 Installation

To install TabArena, ensure you are using Python 3.9-3.11. Then, run the following:

git clone https://github.com/autogluon/tabrepo.git
pip install -e tabrepo/[benchmark]

📄 Publication for TabArena

If you use TabArena in a scientific publication, we would appreciate a reference to the following paper:

TabArena: A Living Benchmark for Machine Learning on Tabular Data, ick Erickson, Lennart Purucker, Andrej Tschalzev, David Holzmüller, Prateek Mutalik Desai, David Salinas, Frank Hutter, Preprint., 2025

Link to publication: arXiv

Bibtex entry:

@article{erickson2025tabarena,
  title={TabArena: A Living Benchmark for Machine Learning on Tabular Data}, 
  author={Nick Erickson and Lennart Purucker and Andrej Tschalzev and David Holzmüller and Prateek Mutalik Desai and David Salinas and Frank Hutter},
  year={2025},
  journal={arXiv preprint arXiv:2506.16791},
  url={https://arxiv.org/abs/2506.16791}, 
}

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Relation to TabRepo

TabArena was built upon TabRepo and now replaces TabRepo. To see details about TabRepo, the portfolio simulation repository, refer to tabrepo.md.