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. Explore the latest results on the live leaderboard.

This single codebase powers two complementary benchmarks that share the same fitting, runner, and evaluation code:

• 🏟️ TabArena-v0.1 — the living benchmark on curated, IID tabular datasets.
• 🌍 BeyondArena — a holistic, beyond-IID benchmark spanning IID, temporal, and grouped tasks across a wide range of dataset sizes and feature dimensionalities. BeyondArena will superseed TabArena-v0.1 in the future.

Tip New here? Start with TabArena, then graduate to BeyondArena. Get your model working and competitive on TabArena's curated IID datasets first; once it holds up there, run the same code on BeyondArena to stress-test how well it generalizes beyond IID.

TabArena covers 51 curated datasets (9–30 splits each) and 27+ methods, including 10+ tabular foundation models — over 50M trained models, with all validation and test predictions cached for tuning and post-hoc ensembling. BeyondArena extends this to 142 datasets across IID, temporal, and grouped task types, spanning tiny to 1M-row datasets and low- to high-dimensional features.

⚡ Quickstart

Tip The fastest way to try TabArena end-to-end:

pip install uv
git clone https://github.com/autogluon/tabarena.git && cd tabarena
uv venv --seed --python 3.12 && source .venv/bin/activate
uv pip install --prerelease=allow -e "./packages/tabarena[benchmark]"
python examples/benchmarking/run_quickstart_tabarena.py

For other install paths (eval-only, editable AutoGluon, dependency), see Installation below. To try BeyondArena instead, run python examples/beyondarena/run_quickstart_beyondarena.py with the same install.

🕹️ Use Cases

We share more details on various use cases of TabArena in our examples:

• 🌍 Benchmarking Beyond IID (BeyondArena): please refer to examples/beyondarena.
• 📊 Benchmarking Predictive Machine Learning Models: please refer to examples/benchmarking.
• 🚀 Using SOTA Tabular Models Benchmarked by TabArena: please refer to examples/running_tabarena_models.
• 🧪 Advanced and Specialized Usage: please refer to examples/advanced.
• 🗃️ Analysing Metadata and Meta-Learning: please refer to examples/meta.
• 📈 Generating Plots and Leaderboards: please refer to examples/plots.
• 🔁 Reproducibility: we share instructions for reproducibility in examples.

Datasets

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

More Documentation

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

🪄 Installation

Important Requires Python 3.11–3.13 and uv.

TabArena is a uv workspace; its installable packages live under packages/ (tabarena, bencheval, tabflow_slurm). Install the tabarena package directly from packages/tabarena with the extras you need. The --prerelease=allow flag is required so uv resolves the pre-release dependency.

First clone the repo and create a virtual environment (one time):

git clone https://github.com/autogluon/tabarena.git
cd tabarena
uv venv --seed --python 3.12
source .venv/bin/activate

Then pick the install path that matches what you want to do:

uv pip install --prerelease=allow -e "./packages/tabarena[plot]"

uv pip install --prerelease=allow -e "./packages/tabarena[benchmark]"

uv pip install --prerelease=allow -e "./packages/tabarena[benchmark,extended]"

uv pip install --prerelease=allow -e "./packages/tabarena[benchmark,xrfm]"

uv venv --seed --python 3.12 .venv
source .venv/bin/activate

git clone https://github.com/autogluon/autogluon.git
./autogluon/full_install.sh

git clone https://github.com/autogluon/tabarena.git
uv pip install --prerelease=allow -e "./tabarena/packages/tabarena[benchmark]"

uv pip install --prerelease=allow "tabarena[plot]"   # or: pip install --pre "tabarena[plot]"
uv pip install --prerelease=allow bencheval          # leaderboard engine only

# TabArena depends on a pre-release of AutoGluon, so allow pre-releases when installing
# (e.g. `uv pip install --prerelease=allow ...` or `pip install --pre ...`).
# Alternatively, pin AutoGluon to a specific pre-release (an exact `==` pin resolves a
# pre-release without the flag), e.g. add `"autogluon.tabular==1.5.1b20260626"`.

# From PyPI (experimental pre-releases; each tabarena release pins its matching bencheval):
"tabarena>=0.1.0a1"
# From git (tip of main; publishable to PyPI only as a source-only extra, see issue #495):
"tabarena @ git+https://github.com/autogluon/tabarena.git#subdirectory=packages/tabarena"

📦 TabArena Artifacts

TabArena caches predictions, results, and leaderboards as downloadable artifacts so you can reproduce or extend any analysis without re-running the benchmark.

📄 Citation

If you use this code in a scientific publication, please cite the relevant paper(s): TabArena for the living IID benchmark, and BeyondArena for the beyond-IID benchmark.

TabArena

TabArena: A Living Benchmark for Machine Learning on Tabular Data Nick Erickson, Lennart Purucker, Andrej Tschalzev, David Holzmüller, Prateek Mutalik Desai, David Salinas, Frank Hutter NeurIPS 2025, Datasets and Benchmarks Track

📄 arXiv · 🎤 NeurIPS poster & video

@article{erickson2026tabarena,
  title   = {TabArena: A Living Benchmark for Machine Learning on Tabular Data},
  author  = {Erickson, Nick and Purucker, Lennart and Tschalzev, Andrej and Holzm{\"u}ller, David and Desai, Prateek and Salinas, David and Hutter, Frank},
  journal = {Advances in Neural Information Processing Systems},
  volume  = {38},
  year    = {2026}
}

BeyondArena

Beyond IID: How General Are Tabular Foundation Models, Really? Lennart Purucker, Andrej Tschalzev, Nick Erickson, Gioia Blayer, David Holzmüller, Alan Arazi, Alexander Pfefferle, Mustafa Tajjar, Gaël Varoquaux, Frank Hutter

📄 arXiv

@misc{purucker2026beyondiid,
  title         = {Beyond IID: How General Are Tabular Foundation Models, Really?},
  author        = {Purucker, Lennart and Tschalzev, Andrej and Erickson, Nick and Blayer, Gioia and Holzm{\"u}ller, David and Arazi, Alan and Pfefferle, Alexander and Tajjar, Mustafa and Varoquaux, Ga{\"e}l and Hutter, Frank},
  year          = {2026},
  eprint        = {2606.30410},
  archivePrefix = {arXiv},
  primaryClass  = {cs.LG},
  url           = {https://arxiv.org/abs/2606.30410}
}

Relation to TabRepo

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

Research code

This repository contains research code intended for academic research and experimentation. It is not production-ready and should be reviewed, tested, and secured before use in production.